Machine learning models made using artificial intelligence provide computing systems the ability to learn and improve with experience without additional programming. To advance technology, software developers have increased using machine learning models to provide more advanced or intelligent computing systems to their end users. Software developers design most machine learning models for execution by high-powered computing systems, such as desktop computers, with a sustained energy source (e.g., an electrical outlet) because the machine learning models may include complex algorithms and/or models that require substantial processing speed and/or memory.
End users have, however, increased their use of mobile devices that run mobile applications that impact their daily lives. In response, software developers have created mobile machine learning model frameworks to execute (e.g., run) machine learning models, including those frameworks designed to execute on desktop computers and on mobile devices. However, mobile devices are often not high-powered and have limited access to energy sources (e.g., a battery). Thus, the machine learning algorithms often negatively affect mobile devices before the machine learning models can complete their execution, that is, machine learning algorithms may cause mobile devices to overheat, overload the mobile device's memory or processors, or drain the mobile device's batteries.
Thus, there is a clear motivation to prevent machine learning models from negatively affecting mobile devices during operation. In view of these and other shortcomings and problems with machine learning models on mobile devices, improved systems and techniques are desirable.